printer friendly pageCS 327A: Advanced Robotic Manipulation
Advanced control methodologies and novel design techniques for complex human-like robotic and bio mechanical systems. Class covers the fundamentals in operational space dynamics and control, elastic planning, human motion synthesis. Topics include redundancy, inertial properties, haptics, simulation, robot cooperation, mobile manipulation, human-friendly robot design, humanoids and whole-body control. Additional topcs in emerging areas are presented by groups of students at the end-of-quarter mini-symposium. Prerequisites: 223A or equivalent.
Terms: Spr
| Units: 3
Instructors:
Khatib, O. (PI)
CS 328: Topics in Computer Vision
Fundamental issues of, and mathematical models for, computer vision. Sample topics: camera calibration, texture, stereo, motion, shape representation, image retrieval, experimental techniques. May be repeated for credit. Prerequisites: 205, 223B, or equivalents.
| Repeatable
for credit
CS 329: Topics in Artificial Intelligence
Advanced material is often taught for the first time as a topics course, perhaps by a faculty member visiting from another institution. May be repeated for credit.
| Repeatable
for credit
CS 334A: Convex Optimization I (CME 364A, EE 364A)
Convex sets, functions, and optimization problems. The basics of convex analysis and theory of convex programming: optimality conditions, duality theory, theorems of alternative, and applications. Least-squares, linear and quadratic programs, semidefinite programming, and geometric programming. Numerical algorithms for smooth and equality constrained problems; interior-point methods for inequality constrained problems. Applications to signal processing, communications, control, analog and digital circuit design, computational geometry, statistics, machine learning, and mechanical engineering. Prerequisite: linear algebra such as
EE263, basic probability.
Terms: Win, Sum
| Units: 3
Instructors:
Boyd, S. (PI)
;
Nasiri Mahalati, R. (PI)
CS 340: Topics in Computer Systems
Topics vary every quarter, and may include advanced material being taught for the first time. May be repeated for credit.
CS 341: Project in Mining Massive Data Sets
Team project in data-mining of very large-scale data, including the problem statement and implementation and evaluation of a solution; some lectures on relevant materials will be given: Hadoop, Hive, Amazon EC2; other topics of possible relevance to some projects: computational advertising and the adwords problem; graph partitioning and community detection; extracting relations from the Web; stream data processing.
Terms: Spr
| Units: 3
Instructors:
Rajaraman, A. (PI)
;
Ullman, J. (PI)
CS 344G: (Your) Great Ideas for Networked Applications
Graduate project class on computer networking, emphasizing end-to-end applications and protocols. Students will propose and execute an original project in teams of 2-3, culminating in a final writeup and presentation/demonstration. Each week, students will read, present, and lead a discussion about a seminal paper or system. Prerequisites: programming experience;
CS 244 recommended but not required.
Terms: Win
| Units: 3
Instructors:
Winstein, K. (PI)
CS 346: Database System Implementation
A major database system implementation project realizes the principles and techniques covered in earlier courses. Students independently build a complete database management system, from file structures through query processing, with a personally designed feature or extension. Lectures on project details and advanced techniques in database system implementation, focusing on query processing and optimization. Guest speakers from industry on commercial DBMS implementation techniques. Prerequisites: 145, 245, programming experience in C++.
Terms: Spr
| Units: 3-5
Instructors:
Re, C. (PI)
CS 347: Parallel and Distributed Data Management
The principles and system organization of distributed and parallel databases. Data fragmentation and distribution, distributed database design, query processing and optimization, distributed concurrency control, reliability and commit protocols, and replicated data management. Data management in peer-to-peer systems. Data management in the "cloud" using map-reduce and other massive parallelism techniques.
Instructors:
Cooper, B. (PI)
CS 348B: Computer Graphics: Image Synthesis Techniques
Intermediate level, emphasizing high-quality image synthesis algorithms and systems issues in rendering. Topics include: Reyes and advanced rasterization, including motion blur and depth of field; ray tracing and physically based rendering; Monte Carlo algorithms for rendering, including direct illumination and global illumination; path tracing and photon mapping; surface reflection and light source models; volume rendering and subsurface scattering; SIMD and multi-core parallelism for rendering. Written assignments and programming projects. Prerequisite: 248 or equivalent. Recommended: Fourier analysis or digital signal processing.
Terms: Spr
| Units: 3-4
Instructors:
Hanrahan, P. (PI)
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